{"doi":"10.5194/cp-2019-81","title":"Proxy surrogate reconstructions for Europe and the estimation of their uncertainties","abstract":"<jats:p>Abstract. Combining proxy information and climate model simulations allows reconciling both sources of information about past climates. This, in turn, strengthens our understanding of past climatic changes. The analogue or proxy surrogate reconstruction method is a computationally cheap data assimilation approach to benefit from the advantages of both data sources. We use the approach to reconstruct European summer mean temperature from the 13th century until present using the Euro 2k set of proxy-records and a pool of global climate simulation output fields. Previous applications of the analogue method to combine proxy records and simulations did not provide uncertainty ranges. Here, we provide several ways of estimating reconstruction uncertainty for the analogue method, which take into account the non-climate part of the variability in each proxy record. In general, our reconstruction agrees with the Euro 2k reconstruction, which had been conducted with two different statistical methods and using no information from model simulations. At interannual timescales, differences between our reconstruction and the Euro 2k reconstructions may be large, but they are much smaller at multi-decadal timescales. In both methodological approaches, the decades around year 1600 CE were the coldest. The approaches do not agree, however, on the warmest preindustrial decades, which the Euro 2k reconstruction places in the early 15th century and the analogue approach in the early 18th century. The surrogate reconstructions also represent the local variations of the observed proxies even under uncertainty but local uncertainties of the temperature reconstructions tend to be large in areas that are poorly covered by the proxy records. Uncertainties highlight the ambiguity of field based reconstructions constrained by a limited set of proxies.</jats:p>","journal":null,"year":null,"id":635440,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1648592,"name":"Eduardo Zorita","orcid":"0000-0002-7264-5743","position":1,"is_corresponding":false},{"id":1648590,"name":"Oliver Bothe","orcid":"0000-0002-6257-8786","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Proxy surrogate reconstructions for Europe and the estimation of their uncertainties","abstract":"<jats:p>Abstract. Combining proxy information and climate model simulations allows reconciling both sources of information about past climates. This, in turn, strengthens our understanding of past climatic changes. The analogue or proxy surrogate reconstruction method is a computationally cheap data assimilation approach to benefit from the advantages of both data sources. We use the approach to reconstruct European summer mean temperature from the 13th century until present using the Euro 2k set of proxy-records and a pool of global climate simulation output fields. Previous applications of the analogue method to combine proxy records and simulations did not provide uncertainty ranges. Here, we provide several ways of estimating reconstruction uncertainty for the analogue method, which take into account the non-climate part of the variability in each proxy record. In general, our reconstruction agrees with the Euro 2k reconstruction, which had been conducted with two different statistical methods and using no information from model simulations. At interannual timescales, differences between our reconstruction and the Euro 2k reconstructions may be large, but they are much smaller at multi-decadal timescales. In both methodological approaches, the decades around year 1600 CE were the coldest. The approaches do not agree, however, on the warmest preindustrial decades, which the Euro 2k reconstruction places in the early 15th century and the analogue approach in the early 18th century. The surrogate reconstructions also represent the local variations of the observed proxies even under uncertainty but local uncertainties of the temperature reconstructions tend to be large in areas that are poorly covered by the proxy records. Uncertainties highlight the ambiguity of field based reconstructions constrained by a limited set of proxies.</jats:p>","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W4239167708","authors":[],"funders":[{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"ZO133/6-2","title":null},{"funder_name":"Bundesministerium für Bildung und Forschung","grant_id":"01LP1509A","title":null},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"unidentified","title":"unidentified"}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2019,"count":1},{"year":2020,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.5194/cp-2019-81","host_type":""},{"url":"https://doi.org/10.5194/cp-2019-81","host_type":""},{"url":"https://www.clim-past-discuss.net/cp-2019-81/cp-2019-81.pdf","host_type":"publisher"},{"url":"https://doi.org/10.5194/cp-16-341-2020","host_type":""},{"url":"https://cp.copernicus.org/articles/16/341/2020/cp-16-341-2020.pdf","host_type":""},{"url":"https://cp.copernicus.org/articles/16/341/2020/","host_type":""},{"url":"https://doaj.org/article/d3bb4bf57c9b4fd6b91f9b19f4f96bec","host_type":""},{"url":"https://dx.doi.org/10.5194/cp-16-341-2020","host_type":""},{"url":"https://publications.hereon.de/id/37072","host_type":""}],"fields_of_study":["Tree-ring climate responses","Climate variability and models","Plant Water Relations and Carbon Dynamics","01 natural sciences","0105 earth and related environmental sciences"],"mesh_terms":[],"keywords":["Proxy (statistics)","Climatology","Data assimilation","Climate change","Climate model","Econometrics","Environmental science","Meteorology","Computer science","Geology","Geography","Mathematics","Machine learning","Environmental sciences","TD172-193.5","TD169-171.8","GE1-350","Environmental protection","Environmental pollution"],"sdg_mappings":[{"sdg_number":13,"sdg_label":"13. 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